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Distributed Information Sharing

1.
DISTRIBUTED INFORMATION SHARING and PREDICTION
Emel METEOGLU
12200498
Abstract
Effective information sharing in a network-centric system is a golden key for success of
the system. Information sharing is a difficult issue in large network-centric systems
because of complexity and evolving nature of the network topology. The aim of this
paper is to understand the nature of the concepts of distributed information sharing (DIS)
in a network-centric system. Our focus is on trying to explain DIS environment and
challenges and advantages of DIS environment. Situation assessment is another important
subject that we address in this paper in order to develop an intelligent prediction agent in
DIS environment. Although much related work has been done on efficient situation
assessment, most work is based on assumptions which are not suited to network-centric
systems. In another section, we highlight prediction in situation assessment under DIS
environment. In last section, new solutions such as evolutionary programming are
discussed as an effective tool for prediction under DIS environment.
I. Introduction
The paper presents a concept for distributed information sharing for network-centric
systems. Distributed Information sharing (DIS) is a collection of knowledge about nodes
in a system. Each of these nodes has an ability to run their own data fusion process and
these nodes also take place in a specific data topology with specific features. All nodes in
the system are interconnected and each has autonomous processing capability serving
local applications. Each node can execute only one job or it can also work together with
other nodes for global applications. Such applications require data from more than one
site [1].
The first step of DIS is collecting data through intelligence, surveillance, and
reconnaissance activities. Intelligence provides obtaining data through observation,
investigation or analysis. Surveillance means that “systematic” observation to collect
available data. Reconnaissance is a specific mission to obtain specific data. The second
step is transforming that raw data to the required human understandable in real time
supporting information superiority. Information superiority is the capability to collect,
process, and disseminate an uninterrupted flow of information while exploiting or
denying an adversary's ability to do the same. It can also be said that DIS provides deliver
the right information to the right person at the right place at the right time. DIS also
provides to allow humans to quickly gain knowledge and understanding. It deploys all

2.
decision support tools to all command levels leading to Decision Superiority. Decision
superiority provides better decisions arrived at and implemented faster than an opponent
can react, or in a non-combat situation, at a tempo that allows the force to shape the
situation or react to changes and accomplish its mission. Decision superiority does not
automatically result from informational superiority. Organizational and doctrinal
adaptation, relevant training and experience, and the proper command and control
mechanisms and tools are equally necessary. DIS supports effect based mission execution
and monitoring with seeking execution superiority which is the ability to execute well
designed plans faster than the opponent is crucial for success in any system [2].As a
result, it can be noticed that execution superiority is based on decision superiority and
decision superiority is based on information and knowledge superiority.
Human Process
Decision
Superiorit
y
Knowledge Orders/Plans
Execution
Informatio Superiorit
n y
Superiority
Information Fast Strategic
Response
Network-Centric Capabilities
Automated Process Raw Data Reaction (Change)
Complex Environment
Figure 1: DIS structure

3.
DIS has been becoming more important as systems become more complex and
technology intense. There are many applications of DIS. Some of them can be
categorized as [3]:
• National/Local Warning System
• National/Local Border Management System
• Incident Management System
• Response and Recovery System
• Military Support System
The purpose of DIS is to develop an intelligent real time prediction agent for situation
assessment under DIS environment. The reason for this is that sometimes valuable data is
geographically distributed and this information can be extremely valuable to decision
makers in taking both proactive as well as reactive measures designed to ensure effective
functioning of system. Decision-makers in systems, in order to be able to effectively
perform their responsibilities, need to have critical information in a timely fashion.
Potential hotspots and major crisis can be prevented when system faces with them, if
decision maker have the right information at the right time and in right format. However,
given the complex dynamics and increasingly interdependent nature of systems, it is
almost impossible to completely avoid unanticipated developments or crises. In a crisis, it
is also important to deliver the data to decision makers without overwhelming them with
large amount of irrelevant data. Well structured DIS also make possible to discover of
hidden regularities or patterns in specific domains. Thus, knowledge so gathered can be
useful in the decision making process.
II. Advantages and Challenges of DIS Environment
To guarantee safe information sharing in distributed environment is difficult. Large
volume of data, distributed access control mechanism can not be handled by traditional
centralized information sharing model [4]. Therefore, it can be said that one of the most
important advantages of DIS is accessibility. In DIS environment, if any node or
communication link is fail, the result is a gradual degradation in network performance
rather than catastrophic failure. From another node, replicated copy of data can be
accessible even the node is failed. It means that there is no single critical element for
operation of the network.
Accessibility and availability of the data in DIS environment also provide to increase in
reliability of the system. Since, there is no central and critical node in the network,
necessary information can still be delivered to the right person and at right place. Even a
system comes across a failure; reliability of the system remains high because of
accessibility of the data from another node.
In DIS environment, data is stored close to the anticipated point of use. Thus, data can be
dynamically moved to where it is needed. Moreover, replicated copy of the data also
moved, if the local application of the data is changed. This flexibility increases the

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efficiency of the network. Having the data at the right place and at the right time depend
on this feature. With flexible nature of DIS, there will be no time consuming information
sharing process.
The result of having evolving nature, sometimes adding new nodes to the network is
required in DIS environment. For example, growing in data-mining tasks may necessitate
an increase in number of nodes in the network. In this kind of a situation, only little or no
upheaval of the system occurs. Capacity of the DIS environment and incremental growth
feature enable the system adapting the changes without re-building or re-designing the
network.
Besides of many advantages, DIS environment has also disadvantages. Since information
are transmitted between people and programs in distributed environment, each node of
network either people or program may not trust each other [1]. To secure the data sharing
of information, common security models have two goals. First, protect information from
adversaries to destroy it. The second is preventing the information from disseminating to
other authorized users. Access control mechanism of DIS supports first goal well. Access
control refers to using the target system to control the behavior of access to information
resources. However, access control is not qualified to second goal. The concept of
controlling information flow is important for security models. It prevents malicious
propagation of information resources. Moreover, most attentions should be directed to
relationships between information resources. Although privacy and security issues are
better than centralized information sharing, these issues still carry risk also in DIS
environment. Cryptographic protocols can be used for enabling privacy-preserving
information sharing in distributed environment. Although they are efficient enough for
practical use but they are also extremely difficult task [5]. On the other hand, other
techniques can be inefficient to be practical. Correspondingly, each node may identify a
large number of candidate anomalous behaviors, which must be compared quickly
against those identified by other nodes. This operation, when conducted with strong
privacy guarantees, can be so expensive as to render the network monitor slow and nearly
useless. These security definitions must be described, completely understood, and
carefully achieved.
As mentioned above, one of the major disadvantages of DIS environment is to control
and monitor the system. It also makes planning issues difficult for the system. Also,
because of distributed environment, during information flow, information leak may
occur. DIS environments are generally complex system of systems. This nature of DIS
environment requires interoperability between subsystems. Communication interface in
the environment is vital issue for success of the system.

5.
Security Flexibility
Reliability&
Availability Privacy
Incremental Information
Growth Leak
Figure2: Attributes of Distributed Information Sharing
III. Situation and Situation Assessment
Situation assessment outlines the process of gathering and analyzing the information
needed to make an explicit evaluation of a system in its environment. It can be defined as
a process of aggregating sensory, non-sensory, and priori input to construct a
representation and evaluation of a situation [1]. It is a necessary step in understanding
information which is related to one’s goals. Also, it helps in improvement of DIS
environment through communication with others. Situation assessment can be described
in four steps: (1) collecting internal and external data, (2) evaluating data’s impact on the
network, (3) analyzing data and, (4) defining strategies for data-related situation. At the
conclusion of a situation assessment, a strategic planner will have a database of quality
information that can be used to make decisions and a list of critical issues which demand
a response from the system.
Situation is a meaningful abstraction of personal, group, behavioral and/environmental
information relevant to the goal of data-mining specific agent [6]. Each agent is one of
the DIS nodes. Each agent has some goals or tasks to do. Agents can reach their goals by
themselves or they can share the goal with other members in the group. Each agent has
their own experience held within their memory and they observe different kind of
environmental situations. However, an agent can have knowledge which can be needed
by other agents to maintain successful performance [7].

6.
Is situation No
assessment
necessary?
Yes
Data Data Data Prediction
Collection Evaluation Analysis
(DM)
Design
Share the strategies for
information situation
Figure 3: Situation Assessment Process
Although skills involved in situation assessment are not yet well understood, improved
situation assessment ability may lead to faster, better planning. There are some important
structures which are helpful in situation assessment. First of all, long-term memory
structures are useful in organizing structures about enemy’s goals, intentions, strengths,
and weakness. Another structure is value/actions structures which reflect a qualitatively
different way of viewing knowledge. Moreover, meta-cognitive processes shape and
guide the retrieval of knowledge from long-term memory structure and its synthesis in a
model and/or plan for the current situation. However, sometimes resolve one kind of a
problem can create other problems. For example, in situation assessment process, making
new assumptions about enemy’s intentions or capabilities to solve conflicting data
renders the situation assessment process unreliable. In this sense, detecting such
unreliability depends crucially on remembering past assumptions. Situation assessment
requires skills in monitoring and regulating cognition of situation [8].
On the other hand, there are also some key factors which affect the processing of
situation assessment within an agent. Temporal relations between perceived sensory data
are critical to sense the situation. Another thing is the agent’s expectation and prior
experiences about the meaningfulness of the sensory data because an agent acts
depending on its memory. Also when an agent has a piece of information, it delivers the
information to its neighbors who need it mostly. An agent does that with basing on
related messages previously received. The agent capacity is another factor because agents
are resource bounded. Therefore they should not spend so much time for information
sharing. The agent’s mental attitudes such as beliefs, wishes, desires, goals, and the
agent’s emotions such as fear, anxiety, and joy also affect the success of information

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sharing [1]. For example, an agent’s beliefs can determine which information is worthy
enough to take its attention. In this sense, it can be said that the inclusion of all of these
influencing factors requires extremely high-level of DIS architecture.
IV. Data Mining in DIS Environment
Generally, data mining (DM) is the process of analyzing data from different perspectives
and summarizes it into useful information. Data mining can also be described as
extracting information which is previously unknown and potentially useful- from large
databases [9]. Data mining is a process which starts with analysing data to show patterns
or relationships and then sorting data through large amount of data. It also includes
picking out pieces of relative information or patterns. Upper-level data mining methods
are prediction, characterization, classification and clustering. At the upper level, data is
randomly separated into train and test sets. The training set is given to the bias optimizer
(the lower level) that searches for the best point in the bias space based on an internal
train test evaluation. The best point in bias space is applied to all the training examples,
and the resulting function is returned to the upper level for final evaluation against the
"unseen" upper level testing examples [10].
The major purpose of DM in a DIS environment is to make Situation Assessment (SA).
Data analyzing is the one of the steps of SA. Well performed DM process makes SA
more efficient. After DM process, strategies for the situation are designed and this
information is shared with other nodes.
The distributed nature of the information in DIS environment is hidden from the upper
level data-mining agents and this transparency manifests itself in a number of ways.
For upper level data-mining agent, “One-Stop” virtual gateway must be provided by DIS
environment to ensure enough data integration. Integration of the nodes is provided by
these gateway agents. They do not act as a center of information sharing. They function
as a broker. They establish a relationship which is called subscription between source of
the data and user of the data. The purpose of the subscription is to maintain currency of
the data. If data changes, the source of the data should inform gateway agent and likewise
user of the data should be informed about changes by gateway agent. It can be said that
every communication interfaces between source and user of the data can be handled by
gateway agents. Since the idea under DIS is being not centralized, communication
between distributed nodes is provided by gateway agent interface. It also provides secure
path between nodes of the network with authentication and to access the external
environment of the network.

8.
`
Local Users
Data Node 1
Virtual “One-stop”
`
Gateway
Data Source for DM
Data
Local Users
Upper Level DM Agent
Data Node 3
`
V. Prediction in Situation Assessment and DIS Environment Local Users
Data Node 2
Figure 4: DM in DIS Environment (Retrieved from [11])
V. Prediction under DIS Environment
The ultimate purpose of the data mining is prediction. Predictive data mining is the most
common and also most difficult type of data mining. Moreover, it is the one that has most
direct applications in both business and military. The prediction is the core activity of the
SA analysis. It provides basis for any type of further actions such as mitigation,
preparedness, planning and emergency response.
The values/patterns of predictive targets in a DIS environment usually exhibit highly non-
stationary dynamics because it evolves along with the evolution of environment. Since
the short-term performance of DM agents in DIS environment can be measured, it is
possible to integrate reinforcement learning philosophy with traditional teacher-based
prediction framework. Such integration is breakthrough idea in this domain.
Reinforcement provides history for further predictions and further predictions depend on
previously reinforcement learning process [11].
Prediction is important because of the reasons that are mentioned above, so data-mining
necessitates some characteristics. For efficient and effective data-mining, tools which are
used for prediction should provide accurate and realistic solutions. It enables to increase
performance of the data-mining process. Also, tools should allow users to perform
customize objectives. Besides these, tools should be able to cope with evolving nature of
DIS environment [12].

9.
In the next section, data-mining tools which can cope with evolving and complex nature
of the DIS environment are presented. These tools can tackle different requirements and
challenges of prediction under DIS environment. Although there is no concrete solution
which is proposed, conceptual descriptions of tools for prediction challenges are given
and it is understood that combination of different tools is an efficient way for better data-
mining.
VI. Proposed Solutions for Prediction under DIS Environment
While the complexity of DIS environment has been increasing, the need for new
systematic techniques has been realized by researchers. Also, because of distributed
nature, decentralization makes control of information sharing difficult. The need for more
robust and adaptive techniques for prediction under DIS environment is essential in
information sharing. More useful techniques should address the basic issues in the DIS
and give effective solutions for prediction. In this sense, evolutionary techniques which
consist of the genetic algorithm (GA), evolutionary programming (EP), evolutionary
strategies (ES), and genetic programming (GP) provide a basis for understanding the
problems of prediction under DIS environment.
Dealing with using conventional large engineering process is crucial problem because of
the distributed nature of information sharing. Therefore, creating an environment in
which continuous innovation can occur which is also called evolutionary process is
becoming better solution. The concept of evolution depends on that many different
systems can exist at same time and they can be affected a change in parallel [13]. The
effective solutions from evolution techniques are gained from the feedback mechanism.
Most recent programming strategies such as spiral development, extreme programming,
and open source movement consult the features of evolutionary techniques.
According to the GA/EA approach, automation of the design process is occurred by
transferring the whole problem into a computer. Therefore, representation of possible
systems, identifying utility function, implementation of selection and replication and also
creating the system design in computer should be developed by systems engineers. It
depends on using advantages of both capability of human being and computers [13].
Creating an environment which innovations and creative changes take place is the
fundamental concept of evolutionary process. Evolutionary process also assumes that
even the systems have same components in different parts of the system, the effect of the
changes are not occurred at the same time. Therefore, development of environment
should be built in such a way that enables us to accomplish the exploration of
possibilities in a fast manner [13].
On the other hand, the conventional techniques which are used in large engineering
systems are not entirely abandoned in the evolutionary context. Instead, they should be
used to extend the concept of evolutionary process. Well known and tested strategies for

10.
planning, specification, design, implementation and testing can be used by the developing
parts of the system which are individuals or teams [13].
Besides the GA, there are also some computer programming approaches that have
emerged to analyze, simulate and model the characteristics of DIS environment. One of
them is Artificial Life program that simulates the evolutionary process of life which
chooses the survivors with computer generated agents on computer. Agents communicate
with the environment receive and transmit information from and to the environment.
Communication and rules in the agent-based modeling models the environment of
distributed information sharing. Another approach is Neural Network in which an
interconnected group of artificial nerve cells affect each other to reach a result based
upon their inputs. The simulation continues until the best matches for the answer is
reached. It is commonly used classification problems in data-mining. Data-mining
generally requires smart architecture, user interactions and performance. Neural network
which is combined with GA provide an evolving architecture to select the inputs from
environment and control its topology. Besides these, it can be proposed that Adaptive
Critic Designs (Heuristic Dynamic Programming, Dual Heuristic Programming, and
Globalized Dual Heuristic Programming) and Q-learning will be considered as potential
reinforcement learning candidates. Whatever the solution is, there are three key factors
that should be considered in the solution: (1) system state, (2) reinforcement reward
signal, and (3) actions that an agent can take. Supervised learning blocks such as
Artificial Neural Networks, Fuzzy Association System will be used to make coarse
learning and the "normal" system patterns can be generalized from historical data. After
sufficient coarse learning, fine learning is applied which employs reinforcement learning
(RL) algorithms. RL agent will mimic system evolution. The offset will be generated to
show the effects of “unexpected” events [13].
In addition to these, swarm intelligence can be also useful tool to understand the
environment of distributed information sharing because swarm intelligence systems have
common characteristics with DIS such as being distributed, local interactions, being
flexible and robust, and autonomy of units.
VII. Conclusion
For net-work centric systems, information sharing is a crucial for survivability of the
networks. Decentralized information sharing has many advantages such as reliability,
efficiency, capacity and flexibility. On the other hand, evolving nature of DIS
environment makes prediction process much more difficult. In this paper, we describe
environment of DIS and advantages and challenges of it. Then, we focus on situation
assessment and prediction challenges under DIS environment. We highlight importance
of combination of different tools usage to understand and cope with these challenges. It is
obvious that DIS environment necessitates more attention to this area and more focus on
robust solutions. Also, new tools should be added to systems engineers’ tool box to
comprehend and solve DIS problems.